SCALABLE COMPUTATIONAL PLATFORM FOR ACTIVE CLOSED-LOOP BEHAVIORAL CODING IN AUTISM SPECTRUM DISORDER ABSTRACT Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in neurodevelopmental disorders, including autism spectrum disorder (ASD). Such behavioral ratings are subjective, require significant clinician expertise and training, typically do not capture data from the children in their natural environments, and are not scalable for large population screening, low-income communities, or longitudinal monitoring. The development of scalable digital approaches to standardized objective behavioral assessment is thus a significant unmet need in ASD, here addressed via machine learning and computer vision with the goal of providing scalable methods for assessing existing biomarkers, from eye tracking to movement and posture patterns, and tools for novel discovery. Our long-term goal is to develop validated scalable tools for the automatic behavioral analysis of neurodevelopmental disorders. The proposed computational project leverages results and big data derived from our previous studies (N=1,864 participants) and our recently funded NIH Autism Center of Excellence (ACE) award (N=7,436 participants). The ACE project will allow us to develop and validate our tools on several thousand toddlers recruited in Duke pediatric primary care and followed longitudinally for whom gold-standard diagnoses of ASD, attention deficit hyperactivity disorder (ADHD), developmental and language delay and extensive electronic health record (EHR) data will be available; and in a case control study of 224 age-matched groups of young children with ASD, ADHD, and typical development from whom gold-standard diagnostic, extensive phenotypic, Tobii eye- tracking, and EEG will be collected. This project aims to develop novel computational methods using these datasets, from sensing in scalable fashion behaviors such as attention and gaze (Aim 1) and motor/posture (Aim 2), to their multimodal integration (Aim 3). A unique aspect of our computational approach is the closed- loop integration of stimuli design for actively eliciting behavioral symptoms, use of consumer-grade sensors, and automatic behavioral analysis. This contrasts with the current approach of independently selecting stimuli and using expensive lab-based professional grade sensors with off-the-shelf algorithms to capture behavioral biomarkers expected from the stimuli. Our approach involves active elicitation of behavior which is also different from commonly used digital approaches that involve gathering large datasets from passive sensing, such as actigraphy monitoring of spontaneous behavior at home. Our framework results in active closed-loop sensing, where participants are engaged in short and developmentally appropriate activities on ubiquitous devices, while the sensors in the same device capture information for the automatic and quantitative analysis of behavioral biomarkers. This scalable, objective, and standardized way of stimulating, sensing, and analyzing allows the collection of large behavioral datasets for machine learning.